Thrive in Complexity

Failure to recognize the difference between the merely complicated and the truly complex can bring an enterprise to its knees. Too often, the telltale, differentiating cues remain hidden in plain sight by pattern-bound ways of thinking much better suited to the past than to the present and future. Differentiating the complicated from the newer, complex systems that are coming to dominate most aspects of the world around us, both in terms of business products and operations, and systems of thought is more than a useful exercise. Aligning styles of thinking with the specifications of the systems with which people need to engage is fundamental to the successful handling of those systems.

Complicated Systems vs. Complex Systems

Complicated systems and complex systems are similar in that both are likely to consist of numerous parts that ultimately must fit together in coherent ways. Both can be difficult to handle, and both require a good deal of thinking. However, the similarities tend to end here. Complicated systems are comparatively more stable and less subject to change. Things can be anticipated in advance. Once a product or task has been handled successfully, the same procedure or method can be applied to similar products or tasks with a good deal of confidence that similar results will be achieved. The system may consist of many parts and the ways to handle those parts may require a great deal of training and experience to master. That mastery usually requires being trained and experienced in a specialized body of knowledge and skills. For example, civil engineers know the parameters that need to be detailed when designing a bridge. Bridges can be very complicated structures; however, we have been designing and building bridges for many centuries and the fundamental principles of bridge design are well documented. Predictability is inherently a feature of a complicated system. That is, the end goal or desired outcome is known, even if the path to achieve that goal may be long and might contain numerous decision points. However, the length of the path and the decision points are known in advance, and the criteria for turning one way versus another at the decision points are known. With sufficient training and experience, plus appropriate attention to detail, the correct turns can be identified with confidence. Things proceed linearly, from A to B to C, according to one or more established sets of rules or algorithms. Consequently, the handling of complicated systems can be mastered and the correct rules and procedures can be codified. Referring again to bridge design, many factors have to be taken into account in the design of a long suspension bridge: the extremes of weather, currents, and winds, and the weight of materials are examples of the many parameters involved. However, most of these parameters can be estimated accurately from historical records, known properties of m aterials, and from technical specifications. Once they are known, experienced engineers can put together a detailed blueprint for the bridge. Then comes the complicated task of building the bridge. As in all large construction projects, many groups of specialists and skilled workers will be needed. Each group has its specialized job to perform. Integration and coordination during the construction are essential, however, the individual group of specialists need not worry about such matters. They do their individual jobs and the general contractor overseeing the construction assures that the sequencing and integration of parts goes forward according to an established plan. Complex systems are quite different, and a key differentiator is uncertainty. Things are not nearly as predictable. In a complex system, the uncertainty may arise from two different system features: change and multiple interdependencies among the parts. The parts tend to change or evolve, as do the relationships among the parts. Any one part of the system might be influenced by multiple other parts and, in turn, might influence a number of other parts. Linearity vanishes; things cannot be programmed in advance to proceed in a straight sequence from A to B to C. Consequently, the design and configuration of parts might need to be negotiated in real time based on the dynamic state of the system and the multi-directional interdependencies. For instance, in an automated weather prediction system there are many mutually interacting variables that determine predictions: atmospheric pressures at the surface and aloft, air mass positions and movements, and ocean currents, to name a few. Changes in any of them can influence any of the others, moment by moment, as per the classic “butterfly effect.” Such systems are far more accurate now than in the past, aided as they are by global satellite tracking systems. Nonetheless, their accuracy remains relative and far from absolute. As a rule, methods that proved successful for dealing with a particular complex system cannot be counted on to work the same way for handling a similar system in the future. This is because similarity inevitably is a relative quantity in the realm of complex systems. No two systems will be exactly alike. If they were exactly alike, they would not be truly complex systems.

The Complicated Past

The Industrial Revolution sparked a huge leap forward in the design and construction of massive and elaborate machines and products. Roads, ships, automobiles, aircraft, spacecraft, computing systems, factories, and buildings of huge proportions demonstrate this. The rise of scientific management as conceived by Frederick W. Taylor in the early Twentieth Century and as implemented in mass production operations worldwide is perhaps the ultimate expression of rules for handling complicated systems. According to the dictates of scientific management, everything is specified and programmed in advance, right down to the exact moment-by-moment sequence of motions made by workers in those operations. Not surprisingly, in the business world, that complicated past continues to influence operations today. Organizations are set up like elaborate, complicated machines to produce work. Specialized units based on function, product or geography are set up so that each unit can perform its specialized role, leading ultimately to the output of standardized products and services. Employees within each unit are assigned specific “Key Performance Indicators” (KPIs) on which they are exhorted to concentrate their attention and energies. The idea is that if we all just focus on doing our jobs, the right stuff will get produced in the end.

The Complex Present and Future

Change is a hallmark characteristic of complex systems, not complicated systems, and in the new millennium, things are changing and the pace of change is accelerating. Moreover, it is now commonplace for a business’ operations to span the globe. Consequently, the communication requirements within an enterprise must address asynchronous time zones and cross many national, cultural, and market boundaries. In many instances, the demand for new and innovative products and services means that an enterprise must keep moving faster and faster. Companies must stay abreast of swiftly evolving market trends, while dealing with competitive threats that seem to appear instantly, seemingly out of nowhere. New technologies can spring up overnight that can change the entire shape of what once was a stable and longstanding market. Think of Kodak, Xerox, and Motorola, former titans of industry whose business models were rendered almost obsolete right in front of them. One could argue that they had become pattern bound by their past successes in much less complex times, even though their products were built upon highly complicated technologies. At the moment, fast-moving startup firms such as Uber and AirBnB are radically altering parts of the local transportation and hospitality industries while current occupants of those industry segments struggle to compete using traditional practices and procedures. Few enterprises can rest on their laurels and survive. Silo Mindsets Nonetheless, perhaps reflective of the past, complicated systems and the mindsets engendered by them, many products are designed and produced within different units that each has its own standard procedures and highly specific performance objectives. That is, each unit has its own set of KPIs. Often those KPIs are so specific and narrowly focused within each unit that 3 the various units become silos of internal focus. The need for integration becomes eclipsed or is treated as “someone else’s job.” When the work is merely complicated, relegating integration to third parties or individuals higher in a chain of command might work just fine. But where the work is truly complex, a hierarchy simply cannot anticipate and adequately manage the integration challenges, many of which can only be identified and handled on a real-time basis.

Mixed Systems: Complicated and Complex

Even the most complex projects today will involve pockets of highly complicated work. Producing a smartphone requires the efforts of numerous specialists: device engineers, firmware architects and programmers, and materials and packaging specialists, to name a few. In other words, most systems are hybrid combinations of the complicated and the complex. In complicated and complex hybrid systems, integration is the chief challenge. Assuring that the varied parts of a system perform their specialized functions and work together can be a formidable task. One of the biggest challenges arises from the fact that highly complicated work requires a different mindset, or way of thinking, than is needed to handle highly complex work. Yet, in many cases, the mindset best suited to handling complicated work is tasked with the handling of complex work. In order to address the particular challenges of a given system, rather than relying mainly, or only, on standardized management procedures, it is essential to think in a way that mirrors the qualities of the system. Fortunately, styles of thinking can easily be mapped to system qualities.

Styles of Thinking

Just as systems can be described as complicated or complex, so can styles of thinking. Briefly, complicated and complex styles of thinking both fall into a category that my colleagues and I call “maximizing,” or analytic, as distinct from “satisficing,” or action-oriented. Maximizing refers to the tendency to maximize intake of information and analysis of that information when thinking or deciding. Satisficing refers to the tendency to take into account a minimal number of facts or items of information when thinking and deciding – i.e., one is quickly satisfied that a workable solution can be identified by just a few facts. Both categories, maximizing and satisficing alike, styles further differ in the extent that they focus in a structured way on achieving a specific, pre- established goal or outcome as opposed to being responsive to multiple objectives or outcomes that themselves can shift and change as a situation 4 evolves. In our terms, some styles are uni-focused, while others are multi- focused. In dealing with systems, our experience shows that all styles are needed, but not necessarily in equal measure. The less analytic, more action-oriented styles, Decisive and Flexible, are useful for dealing with immediate operational issues that arise: assuring that things are moving forward and that short-term operational problems are addressed and decisions are made. However, in the realm of complicated and complex systems, the more thinking oriented, maximizing styles have a special role. Complicated and complex systems can both be demanding in that a lot of thinking is needed. The maximizer styles, Hierarchic and Integrative, share this quality – both styles are given to doing a lot of thinking. For the purposes of this paper, I will only be giving attention to the two maximizer styles.

The Hierarchic style, shown is an analytic, uni-focused, and structured mode of thinking and deciding. When a Hierarchic thinker takes on a problem requiring a solution, the tendency will be to examine a problem thoroughly and to carefully break it into its constituent parts for study or examination. The analysis normally takes place with a clear objective or desired outcome in mind that serves as the focal point. That objective typically is pre-established based on previous training, experience, logic and/or established policy or rules. With this objective in mind, the thinker will look for a best path or strategy to achieve that goal, ideally one that will stand the test of time. Once a path or strategy to achieve the objective has been identified, the inclination is to put together a detailed plan centered on the key objective and then to stay the course until the objective has been achieved. Clearly, the Hierarchic style is a complicated way of thinking; a mindset that is well suited to handling complicated systems. Its analytic, detail-oriented, and highly procedural and focused qualities fit well with the intricate and structured aspects of such systems.

The Integrative style correspondingly, is a better fit with complex systems. Integrative thinkers can be equally as analytic as their Hierarchic-thinking counterparts, but in a much less structured way. In this mode of thinking, the tendency is to keep one’s peripheral vision fully activated and to look at the broad context surrounding a problem. The thinker is interested in, and notices, how the parts of the system are influenced by contextual factors and how the parts of a problem interact and overlap internally. No two problems or systems are seen as identical; differences and unique features are noticed and take on importance. Moreover, goals and particular outcomes are not fixed; instead, they tend to emerge from the analysis and no one objective is likely to stand out as eclipsing the importance of other objectives. Consequently, with multiple objectives in mind, no one path or strategy is seen as adequate for dealing with a problem. Instead, multiple paths and strategies to achieve multiple objectives will emerge from this way of thinking, and those strategies along with the goals they serve may shift and change as the problem evolves. As an analytic, multi-focused and dynamic way of thinking, the Integrative style fits particularly well with complex systems that contain many interacting parts that shift and change in unpredictable ways. The Integrative thinking process parallels the essential qualities of complex systems.

By: Dr. Ken Brousseau. Ph.D.